陳威男 P 林姿妤 P 曾瑞瑜 P 孫程 CSIE98 F 指導教授 盧文祥老師 高宏宇老師 鄭憲宗老師
Outline Introduction Problem definition Paper's method Spatial –temporal Entropy Image Difference-based Spatial Temporal Entropy Image Implementation Experiment result
Introduction A novel human motion detection method based on entropy is shown in this paper,it is motivated by other’s previous work. Methods by others and by the author will be introduced later.
Problem definition Human motion detection method based on entropy is faced with. Entropy from motion and spatial diversity is very hard to differentiate.
Paper's method Spatial-Temporal Entropy Image Difference-based Spatial Temporal Entropy Image
Spatial-Temporal Entropy Image Entropy: a measure of uncertainty Noise: camera channel noise and noise brought by flickering of light Motion: motion object Pixel’s state change by noise would be in a small range, but those by motion will be large. So the diversity of state at each pixel can be used to characterize the intensity of motion at its position.
Spatial-Temporal Entropy Image W*W*L histogram Pixels used to accumulate histogram for (i,j)
Spatial-Temporal Entropy Image Once the histogram is obtained the corresponding density function for each pixel can be computed by: N : the total number of pixel in the histogram q : the bins of the histogram Q : the total number of bins and Eij is called spatial-temporal entropy of pixel (i,j)
Spatial-Temporal Entropy Image Pixels at edges get higher entropy. Both motion and spatial diversity can cause high entropy and they are very hard to differentiate. This point can lead a false detection of motion.
Difference-based Spatial Temporal Entropy Image Form histogram by accumulate pixels in different images Image noises are Gaussian distributed No motion: Pixels occurs would follows zero-mean Gaussian distributions in several difference images Motion occurs: Pixels used to form the histogram would have higher value in difference images Pixel would distributed in a wide range So entropy obtained this way can denote the intensity of motion.
Step1: Calculate difference images RGB color images are first converted to 256 gray level images, denoted by F(k), where k is the frame number. The difference image D(k) is calculated by (3) in which | ‧ | denote absolute value, and Φ( ‧ ) is the quantization function. In all our experiments, Φ( ‧ ) quantizes the 256 gray levels into Q=20 gray levels.
Step2: Histogram accumulation Histogram H i,j (q) for pixel(i,j) is updated online using simple recursive updates. Specifically, the first L frames, i.e. from D(1) to D(L) are used to accumulate histogram for D(L) using (4): And histograms for the subsequent frames are updated by: Where the constant α is set empirically to control the influence of history frames.
Step3: Obtain DSTEI Each pixel at the k th frame the pdf for pixel(i,j) is formed by normalizing the histogram using (6): Where Γ( ‧ ) is the normalization function. In this paper, the pixel number in each bin of H i,j,k is divided by the total number of pixels in the histogram. P i,j,k has 20 discrete values. After pdf for each pixel is obtained, entropy is calculated using (7):
Step4: Motion object location After obtain the DSTEI image,many methods can be used to derive the motion region. One way is to find the peak of it then apply region growing method to locate the motion object. In this paper, another simple method is simply to binarized the DSTEI image by a threshold T.
Implementation(1/3) 計算 |F(k)-F(k-1)|=| 這個 frame pixel 的 RGB- 上個 frame pixel 的 RGB| D(k)=Φ(|F(k)-F(k-1)|) :256 gray levels → 20 gray levels 判斷 D(k) 落在 1~20 哪個 bin 中 累積每個 pixel 算出來的 D(k) 落在每個 bin 的值 If > σ (i,j)pixel 加入標記 Count<5 Count>=5
Implementation(2/3) |F(k)-F(k-1)|=| 這個 frame pixel 的 RGB- 上個 frame pixel 的 RGB| D(k)=Φ(|F(k)-F(k-1)|): 256 gray levels →20 gray levels
Implementation(3/3) 初始值 : If > σ, pixel(i,j) 加入標記
Experimental Result(1/4) 執行環境 :.Net (visual studio 2005) Code: C# CPU: Core2 Quad Q GHz RAM : 2GB
Experimental Result(2/4) 物體由左向右移 :
Experimental Result(3/4) 物體由左向右移 :
Experimental Result(4/4)